Approximating the DGP of China's Quarterly GDP

We demonstrate that the data generating process (DGP) of China’s cumulated quarterly Gross Domestic Product (GDP, current prices), as it is reported by the National Bureau of Statistics of China, can be (very closely) approximated by a simple rule. This rule is that annual growth in any quarter is equal to annual growth in its previous quarter plus an error term that is only nonzero in the first quarter of each year and with small variance. We show that this rule fits the data for 1992Q1 to 2005Q4 well, for total GDP as well for its three sector-specific components. It also gives accurate forecasts for 2006Q1 to 2009Q4. We also study the time series properties of GDP growth in constant prices, and show that these series behave as random walks, with much larger error variance

Does news on real Chinese GDP growth impact stock markets?

Real GDP growth in China follows a random walk. Also, it has often been suggested that China “cooks its books”, that is to say that governmental officials in China manipulate economic statistics such as GDP growth rate to present the outside world a rosy picture (Foreign Policy, September 3, 2009). If such unreliability is known to stock traders, news on GDP should not impact stock market fluctuations or their volatility. We test this hypothesis for 12 series with daily stock market returns for the years 2006 to and including 2009

Are Chinese Individuals prone to Money Illusion?

Using a unique dataset collected through a well-established survey, which was carried out in China, we examine whether Chinese individuals are prone to money illusion. In contrast to the outcomes for US individuals, we find that the Chinese are more likely to base decisions on the real monetary value of economic transactions. We put these observed differences in findings in perspective by comparing the economic conditions in the US and China

Testing for periodic integration

A periodic autoregressive time-series model assumes that the autoregressive parameters vary with the season. This model can also be represented by a multivariate model for the annual vector containing the seasonal observations. When this multivariate model contains one unit root, a time-series is said to be periodically integrated of order 1. In this paper we propose tests for such a single unit root. These tests for periodic integration are applied to a periodic model for the quarterly German consumption series

Bayesian analysis of seasonal unit roots and seasonal mean shifts

In this paper we propose a Bayesian analysis of seasonal unit roots in quarterly observed time series. Seasonal unit root processes are useful to describe economic series with changing seasonal fluctuations. A natural alternative model for similar purposes contains deterministic seasonal mean shifts instead of seasonal stochastic trends. This leads to analysing seasonal unit roots in the presence of mean shifts using Bayesian techniques. Our method is illustrated using several simulated and empirical data

Did men of taste and civilization save the stage? Theatre-going in Rotterdam, 1860-1916. A statistical analysis of ticket sales

This essay deals with Dutch theater history of the second half of the 19th century (1860—1916). It statistically tests, whether the dominant opinion in Dutch theater writing, that after 1870 the stage recovered from a half century of decline, due to a renewed interest in it by the city elite, occupying the first ranks with a taste for civilized modern drama, and that, hence, a sharp cleft became visible between lower-rank tastes and upper-rank tastes. We test the tenability of this position on the basis of the Rotterdam Grand Theater archives, which contain ticket sales per rank per performance from 1776 till 1916, and the play bills of the performances. We analyze aggregated behavior of an anonymous theater consumers subdivided into price classes, hypothesizing that differences in attendance to high and low quality plays (as the critics judged them) over the different ranks, might reveal class-based divisions of taste. A long-memory time series analysis confirms that there is a significant gradual change of quality in the theater during the period 1860—1881, but this change is hardly rank- (and by implication likely class-) based. A second time series analysis, analyzing the impact of the repertoire and companies controlled for season and dynamics of the time series over the years 1860—1887 and 1887—1916, hardly sustains the narrative of recovery for most products as related to ranks. Only in a few telling instances, there was a clear opposition between low-rank tastes and upper-rank tastes. Hence, the recovery thesis must on the whole be rejected. This research will be followed-up by a prosopographical analysis of season-ticket and coupon holders in the Rotterdam theaters from 1773—1916, in which more detailed information on the social backgrounds and particularly on social class division of not anonymous theater audiences in `the long 19th century' is central

Mean shifts, unit roots and forecasting seasonal time series

Examples of descriptive models for changing seasonal patterns in economic time series are autoregressive models with seasonal unit roots or with deterministic seasonal mean shifts. In this paper we show through a forecasting comparison for three macroeconomic time series (for which tests indicate the presence of seasonal unit roots) that allowing for possible seasonal mean shifts can improve forecast performance. Next, by means of simulation we demonstrate the impact of imposing an incorrect model on forecasting. We find that an inappropriate decision can deteriorate forecasting performance dramatically in both directions, and hence we recommend the practitioner to take account of seasonal mean shifts when testing for seasonal unit roots

Seasonality and stochastic trends in German consumption and income, 1960.1- 1987.4

The quarterly time series of German consumption and income are analyzed with respect to seasonality and stochastic trends. It emerges that both variables can be appropriately described by a periodically integrated autoregression. An implication is that the stochastic trend and the seasonal fluctuations are not independent for each of the univariate series. In order to test for cointegration across the two series, we propose several methods which take account of the relationship between seasons and trends in the univariate series. Some of these methods boil down to extracting the stochastic trend from the univariate series in a first step and to relating these trends using cointegration techniques in a second step. Another method is an extension of the Johansen cointegration testing approach to periodic vector autoregressions. Monte Carlo simulations are used to evaluate the empirical performance of the various methods. The main empirical result is that only in the first quarter there seems to be cointegration between German consumption and income